A Machine Learning and Internet of Things-Based Online Fault Diagnosis Method for Photovoltaic Arrays
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- Mellit, A. & Tina, G.M. & Kalogirou, S.A., 2018. "Fault detection and diagnosis methods for photovoltaic systems: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 91(C), pages 1-17.
- Sufyan Samara & Emad Natsheh, 2020. "Intelligent PV Panels Fault Diagnosis Method Based on NARX Network and Linguistic Fuzzy Rule-Based Systems," Sustainability, MDPI, vol. 12(5), pages 1-20, March.
- Selma Tchoketch Kebir & Nawal Cheggaga & Adrian Ilinca & Sabri Boulouma, 2021. "An Efficient Neural Network-Based Method for Diagnosing Faults of PV Array," Sustainability, MDPI, vol. 13(11), pages 1-27, May.
- José Miguel Paredes-Parra & Antonio Mateo-Aroca & Guillermo Silvente-Niñirola & María C. Bueso & Ángel Molina-García, 2018. "PV Module Monitoring System Based on Low-Cost Solutions: Wireless Raspberry Application and Assessment," Energies, MDPI, vol. 11(11), pages 1-20, November.
- Collin Barker & Sam Cipkar & Tyler Lavigne & Cameron Watson & Maher Azzouz, 2021. "Real-Time Nuisance Fault Detection in Photovoltaic Generation Systems Using a Fine Tree Classifier," Sustainability, MDPI, vol. 13(4), pages 1-15, February.
- Mellit, Adel & Kalogirou, Soteris, 2021. "Artificial intelligence and internet of things to improve efficacy of diagnosis and remote sensing of solar photovoltaic systems: Challenges, recommendations and future directions," Renewable and Sustainable Energy Reviews, Elsevier, vol. 143(C).
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- Adel Mellit & Chadia Zayane & Sahbi Boubaker & Souad Kamel, 2023. "A Sustainable Fault Diagnosis Approach for Photovoltaic Systems Based on Stacking-Based Ensemble Learning Methods," Mathematics, MDPI, vol. 11(4), pages 1-15, February.
- Mellit, A. & Benghanem, M. & Kalogirou, S. & Massi Pavan, A., 2023. "An embedded system for remote monitoring and fault diagnosis of photovoltaic arrays using machine learning and the internet of things," Renewable Energy, Elsevier, vol. 208(C), pages 399-408.
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Keywords
photovoltaic array; machine learning; Internet of Things; fault detection; fault classification;All these keywords.
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